print.bootstrap_f2 function

Print a summary of the bootstrap f2 simulation

This is a method for the function print() for objects of class ‘bootstrap_f2’ .

## S3 method for class 'bootstrap_f2'
print(x, ...)

Arguments

• x: An object of class ‘bootstrap_f2’ returned by the bootstrap_f2() function.
• ...: Further arguments passed to or from other methods or arguments that can be passed down to the print.boot() and print.bootci() functions.

Returns

The ‘bootstrap_f2’ object passed to the x

parameter is returned invisibly.

Details

The elements Boot and CI of the ‘bootstrap_f2’ object that is returned by the function bootstrap_f2() are objects of type ‘boot’ and ‘bootci’ , respectively, generated by the functions boot() and boot.ci(), respectively, from the ‘boot’ package. Thus, the corresponding print

methods are used. Arguments to the print.boot() and print.bootci() functions can be passed via the ... parameter.

Examples

# Bootstrap assessment of data (two groups) by aid of bootstrap_f2() function
# by using 'rand_mode = "complete"' (the default, randomisation of complete
# profiles)
bs1 <- bootstrap_f2(data = dip2[dip2$batch %in% c("b0", "b4"), ],
                    tcol = 5:8, grouping = "batch", rand_mode = "complete",
                    rr = 200, new_seed = 421, use_ema = "no")

# Print of a summary of the assessment
print(bs1)

# STRATIFIED BOOTSTRAP
#
#
# Call:
#   boot(data = data, statistic = get_f2, R = R, strata = data[, grouping],
#        grouping = grouping, tcol = tcol[ok])
#
#
# Bootstrap Statistics :
#   original      bias    std. error
# t1* 50.07187 -0.02553234   0.9488015
#
#
# BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
# Based on 200 bootstrap replicates
#
# CALL :
#   boot.ci(boot.out = t_boot, conf = confid, type = "all", L = jack$loo.values)
#
# Intervals :
#   Level      Normal              Basic
# 90%   (48.54, 51.66 )   (48.46, 51.71 )
#
# Level     Percentile            BCa
# 90%   (48.43, 51.68 )   (48.69, 51.99 )
# Calculations and Intervals on Original Scale
# Some BCa intervals may be unstable
#
#
# Shah's lower 90% BCa confidence interval:
#  48.64613

# Use of 'rand_mode = "individual"' (randomisation per time point)
bs2 <- bootstrap_f2(data = dip2[dip2$batch %in% c("b0", "b4"), ],
                    tcol = 5:8, grouping = "batch", rand_mode = "individual",
                    rr = 200, new_seed = 421, use_ema = "no")

# Print of a summary of the assessment
print(bs2)

# PARAMETRIC BOOTSTRAP
#
#
# Call:
#   boot(data = data, statistic = get_f2, R = R, sim = "parametric",
#        ran.gen = rand_indiv_points, mle = mle, grouping = grouping,
#        tcol = tcol[ok], ins = seq_along(b1))
#
#
# Bootstrap Statistics :
#   original     bias    std. error
# t1* 50.07187 -0.1215656   0.9535517
#
#
# BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
# Based on 200 bootstrap replicates
#
# CALL :
#   boot.ci(boot.out = t_boot, conf = confid, type = "all", L = jack$loo.values)
#
# Intervals :
#   Level      Normal              Basic
# 90%   (48.62, 51.76 )   (48.44, 51.64 )
#
# Level     Percentile            BCa
# 90%   (48.50, 51.70 )   (48.88, 52.02 )
# Calculations and Intervals on Original Scale
# Some BCa intervals may be unstable
#
#
# Shah's lower 90% BCa confidence interval:
#  48.82488

See Also

bootstrap_f2, boot, boot.ci, print.boot, print.bootci, methods.